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This paper introduces Phase-varying Neural Potential Functions (PNPF), a Learning-from-Demonstration (LfD) framework that uses a neural network to learn a potential function conditioned on a phase variable estimated directly from state progression. This approach enables stable and reactive control for tasks with intersections, addressing limitations of both second-order dynamics and open-loop phase-based methods. Experiments show that PNPF outperforms existing baselines on trajectories with intersections and demonstrates robustness in real-time robotic manipulation under external disturbances.
Achieve robust robot control through intersections by learning phase-conditioned potential functions, sidestepping the instability issues of velocity-dependent methods.
Dynamical systems (DS) methods for Learning-from-Demonstration (LfD) provide stable, continuous policies from few demonstrations. First-order dynamical systems (DS) are effective for many point-to-point and periodic tasks, as long as a unique velocity is defined for each state. For tasks with intersections (e.g., drawing an "8"), extensions such as second-order dynamics or phase variables are often used. However, by incorporating velocity, second-order models become sensitive to disturbances near intersections, as velocity is used to disambiguate motion direction. Moreover, this disambiguation may fail when nearly identical position-velocity pairs correspond to different onward motions. In contrast, phase-based methods rely on open-loop time or phase variables, which limit their ability to recover after perturbations. We introduce Phase-varying Neural Potential Functions (PNPF), an LfD framework that conditions a potential function on a phase variable which is estimated directly from state progression, rather than on open-loop temporal inputs. This phase variable allows the system to handle state revisits, while the learned potential function generates local vector fields for reactive and stable control. PNPF generalizes effectively across point-to-point, periodic, and full 6D motion tasks, outperforms existing baselines on trajectories with intersections, and demonstrates robust performance in real-time robotic manipulation under external disturbances.
